import gym
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import rl_utils

class PolicyNet(torch.nn.Module):
    '''定义策略网络，输入是某个状态，输出是该状态下的动作概率分布（Softmax实现）'''
    def __init__(self,state_dim,hidden_dim,action_dim):
        super(PolicyNet,self).__init__()
        self.fc1 = torch.nn.Linear(state_dim,hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim,action_dim)
    def forward(self,x):
        x = F.relu(self.fc1(x))
        return F.softmax(self.fc2(x),dim=1)

class REINFORCE:
    def __init__(self,state_dim,hidden_dim,action_dim,learning_rate,gamma,device):
        self.policy_net = PolicyNet(state_dim,hidden_dim,action_dim).to(device)
        self.optimizer = torch.optim.Adam(self.policy_net.parameters(),lr=learning_rate)
        self.gamma = gamma
        self.device = device
    def take_action(self,state):
        '''根据动作概率分布随机采样'''
        state = torch.tensor([state],dtype=torch.float).to(self.device)
        probs = self.policy_net(state)
        '''使用 PyTorch 创建一个离散概率分布（Categorical Distribution）,输入的是每个类别的概率或未归一化的对数概率'''
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()   # 在这个离散概率分布中，采样一个动作
        return action.item()
    def update(self,transition_dict):
        reward_list = transition_dict['rewards']
        state_list = transition_dict['states']
        action_list = transition_dict['actions']

        G = 0
        self.optimizer.zero_grad()
        for i in reversed(range(len(reward_list))):  # 从最后一步算起
            reward = reward_list[i]
            state = torch.tensor([state_list[i]],dtype=torch.float).to(self.device)
            action = torch.tensor([action_list[i]]).view(-1,1).to(self.device)
            log_prob = torch.log(self.policy_net(state).gather(1,action))
            G = self.gamma * G + reward
            loss = -log_prob * G
            loss.backward()
        self.optimizer.step()

if __name__ == '__main__':
    learning_rate = 1e-3
    num_episodes = 1000
    hidden_dim = 128
    gamma = 0.98
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    env_name = "CartPole-v0"
    env = gym.make(env_name)
    env.seed(0)
    torch.manual_seed(0)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = REINFORCE(state_dim,hidden_dim,action_dim,learning_rate,gamma,device)

    return_list = []
    for i in range(10):
        with tqdm(total=int(num_episodes/10),desc='Iteration %d' % i) as pbar:
            for i_episode in range(int(num_episodes/10)):
                episode_return = 0
                transition_dict = {'states':[],'actions':[],'next_states':[],'rewards':[],'dones':[]}
                state = env.reset()
                done = False
                while not done:
                    action = agent.take_action(state)
                    next_state,reward,done,_ = env.step(action)
                    transition_dict['states'].append(state)
                    transition_dict['actions'].append(action)
                    transition_dict['next_states'].append(next_state)
                    transition_dict['rewards'].append(reward)
                    transition_dict['dones'].append(done)
                    state = next_state
                    episode_return += reward
                return_list.append(episode_return)
                agent.update(transition_dict)
                if (i_episode+1) % 10 == 0:
                    pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
                pbar.update(1)

    episode_list = list(range(len(return_list)))
    plt.plot(episode_list,return_list)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('REINFORCE on {}'.format(env_name))
    plt.show()

    mv_return = rl_utils.moving_average(return_list,9)
    plt.plot(episode_list,mv_return)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('REINFORCE on {}'.format(env_name))
    plt.show()



